CHARACTERIZATION OF SUN AND SHADE CHLOROPHYLL IN WHEAT USING ANGULAR CHRIS/PROBA DATA Oppelt, N. & Mauser, W. Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 München (Germany) n.oppelt@lmu.de w.mauser@iggf.geo.uni-muenchen.de ABSTRACT Chlorophyll is the driving force for photosynthesis of green vegetation; therefore the assessment of chlorophyll is one of the keys for the understanding of photosynthetic processes. Hyperspectral remote sensing techniques for the derivation of chlorophyll and its spatial distribution are already well established. Conventional, nadir-looking sensors are able to monitor the top of canopy, i.e. only the leaves can be monitored. A new generation of sensors such as the satellite-borne CHRIS (Compact High Resolution Imaging Spectrometer) on the platform PROBA (PRoject for On-Board Autonomy) offers angular measurements, which allow an insight into the vegetation underneath the top layer. The system monitors the surface at up to five different along-track angles (± 55, ± 36, nadir). The capabilities of multi-angular imagery for analysing the vertical structure of vegetation canopies were investigated in the scope of this study. The different view angles are investigated for their potential to derive chlorophyll a content not only of top of canopy leaves under direct light, but also of subjacent canopy layers which are influenced mainly by diffuse radiation. In addition, the behaviour of the indices regarding the illumination geometry is investigated. 1. INTRODUCTION Chlorophyll is the driving force for photosynthesis of green vegetation; therefore the assessment of chlorophyll is one of the keys to the understanding of photosynthetic processes. Hyperspectral remote sensing techniques for the derivation of chlorophyll and its spatial distribution are already well established. Conventional, nadir-looking sensors are able to monitor the top of the canopy, i.e. only the leaves can be monitored. A new generation of sensors such as the satellite-borne CHRIS (Compact High Resolution Imaging Spectrometer) on the platform PROBA (PRoject for On-Board Autonomy) offers angular measurements, which may allow an insight into the vegetation underneath the top layer. The system is able to monitor the surface at up to five different along-track angles (± 55, ± 36, nadir). The assessment of leaf pigments using hyperspectral data is now well established. An analysis of impacts of and parts of the canopy to the signal monitored with angular remote sensing is still absent. The chlorophyll content differs between canopy layers which are under a direct or diffuse radiation regime. The chlorophyll content influences the photosynthetic capacity. Therefore the differentiation between canopy layers with different biophysical behaviour would be an important improvement for the assessment of the real conditions of a plant canopy which is crucial for precision farming application as well as for modelling of photosynthesis, vegetation growth and the carbon cycle. With changing view zenith angles the relative fractions of lit and d canopy components are varying [1]. Typically, over vegetation canopies the NIR is more affected by multiple scattering than the RED. This effect causes an increase of the spectral contrast between the NIR and the RED leading to different index values for d canopy components than for components in direct light. This effect as well as varying sensor- geometry superimpose the impact of and chlorophyll content on the signal observed with remote sensing. For this study, indices which are well established for assessing the chlorophyll content using nadir images are applied and analysed for their capabilities of assessing and chlorophyll concerning their behaviour regarding different viewing angles and illumination geometry. The capabilities of multi-angular imagery for analyzing the vertical structure of vegetation canopies are investigated in the scope of a project funded by the German Research Foundation (DFG). 2. TEST SITE AND GROUND MEASUREMENTS The test site Gilching (48 6 N, 11 17 E) is located in the Bavarian Alpine foothills about 25 km south-west of Munich. The test area is located in a subunit of the Munich alluvial plain. A glacial spillway covers most of the area. Its elevation rises gradually from 560m above sea level in the north to 620m in the south. The main soil type is Braunerde, corresponding to cambisols in the FAO system, which covers 90% of the test site. Proc. Envisat Symposium 2007, Montreux, Switzerland 23 27 April 2007 (ESA SP-636, July 2007)
The test area can be assigned to the cool and ever-moist temperate climate. Water stress did not occur during the years investigated. A weather station of the Bavarian network of agro-meteorological stations enables access to local weather monitoring. Station No. 72 (Gut Hüll), located near the test fields, provides meteorological data such as precipitation, soil and air temperature, total radiation and air humidity. Within the test site, two fields of winter wheat (Triticum aestivum L.) grown with the cultivar Achat was chosen as test field for 2004 and 2005. Intensive field measurements were conducted in both years including wet/dry biomass, plant height, phenological stage and chlorophyll content of and leaves. The measurements were carried out at five sampling points along the field diagonals in weekly intervals from April until harvest (July/August). For biomass retrieval the plants were divided into and parts by clipping the stem at the upper end of the first internodium. According to this setting chlorophyll analysis of and leaves was conducted in the laboratory using to the method described by [2]. In addition, field spectrometer measurements and chlorophyll measurements were conducted simultaneously to the CHRIS overpasses. The absorption of radiation by chlorophylls is a primary requirement for photosynthesis. Chlorophyll is directly linked to irradiance. Wheat plants do not produce leaves adapted to d conditions, as every new leaf is exposed to direct radiation. These facts cause a reduced chlorophyll synthesis under d conditions [3,4]. Sun leaves are mainly influenced by direct irradiation while d parts are dominated by a diffuse radiation regime. Due to the differences in radiation at the top of a canopy and beneath, the chlorophyll content is assumed to differ between these two layers. Figure 1: Chlorophyll content per biomass as measured during the vegetation period in 2004 (T = tillering; SE = stem elongation, IE = inflorescence emerges, F = flowering; MD = milk development, D = dough) The field measurements (Fig. 1) confirm this assumption. While the chlorophyll b contents of both and layer stay relatively constant during the year at a certain level, differences between the layers appear for chlorophyll a. Although the mean values for the chlorophyll a content remain relatively constant for both years, a highly dynamic behaviour can be observed with changes in chlorophyll a content of up to 1200 µg/g within a growing period. Therefore the assessment of chlorophyll a is presented in this paper. 3. CHRIS/PROBA DATA CHRIS (Compact High Resolution Imaging Spectrometer) is a satellite-borne sensor on the platform PROBA (PRoject for On-Board Autonomy). The system offers angular measurements and can observe the ground at up to five different along-track angles (± 55, ± 36, nadir). CHRIS can be operated in several modes implying different band settings and geometric resolutions. Mode 5 was used for this study, resulting in 37 spectral bands in the VIS/NIR domain (440 1040 nm) with varying spectral resolution (6 nm at 700 nm to 33 nm at 1033 nm) and a pixel spacing of 17 m at nadir. In total, six CHRIS data sets were acquired in 2004 and 2005: two in 2004 and four in 2005 (Tab. 1). Table. 1: Angular CHRIS data available for growing seasons 2004 and 2005 Date Angular data available -55-36 0 +36 +55 May 25, 2004 July 21, 2004 May 25, 2005 June 2, 2005 June 3, 2005 July 6, 2005 The angles in Tab. 1 correspond to the nominal fly-by zenith angles. The actual observation angles are given in the image header and may differ from the nominal values. For the data mentioned in Tab. 1, the actual observation angles differ from the FZA with a mean of 0.826, a minimum of 0.2 (-55 FZA on June 2 nd 2005), and a maximum value of 6.6 (+36 FZA on June 2 nd 2005). The atmospheric correction and reflectance calibration were conducted using an approach based on MODTRAN 4.2 [3,4]. The modelling of the atmosphere was conducted using the observation zenith and azimuth angles given in the CHRIS header. The nominal FZAs are used in the following for simplification but represent ranges of actual angles around the nominal angles. The validation of the radiometric correction was carried out using field spectrometer measurements. The geometric correction was carried out using ground control points. The geometric correction is important
because pixel spacing differs for each of the angles: the greater the observation angle, the coarser is the geometric resolution. The angular data were resampled to nadir resolution using a nearest neighbour approach. CHRIS operates on a -synchronous orbit (98 ) with a descending path over Gilching. The forward-looking angles (+36, +55 ) therefore are directed towards the and contain a large proportion of shadow, while the backward-looking angles (-36, -55 ) have a parallel view direction hiding most of the shadows. Stable acquisition times near noon (10:33 to 10:47 UTC) result in constant zenith angles (26-28 ). This reduces directional effects due to varying zenith angles. The relative azimuth angles between the and CHRIS are presented in Fig. 2. The Chlorophyll Absorption Integral CAI is a hyperspectral index that has already proven its strong correlation with plant nitrogen and chlorophyll [7]. Its calculation is based on an integral approach that dates from Clark et al. [8]. A straight line is drawn between the reflectance values at 600 and 735 nm (Fig. 3). In a second step, the areas enclosed by this line and below the spectrum are compared. Finally the result is normalized, so that the CAI theoretically returns values from 0 to 100. 4.2. OSAVI The Optimized Soil Adjusted Vegetation Index is easy to use in the context of operational observations of agricultural landscapes. Its determination requires no knowledge of soil properties, and moreover it offered good results for a great variety of agricultural crops investigated by [9]. ( R R )( R + R 0.16) OSAVI = 800 nm / 800nm + (1) 4.3. TCARI/OSAVI The ratio of the Transformed Chlorophyll Absorption in Reflectance Index, which is insensitive to nonphotosynthetic materials, with the OSAVI was proposed by [10]. It combines the characteristics of the TCARI and the OSAVI and showed to be highly sensitive to chlorophyll concentrations while being resistant to variations in leaf area and solar zenith angles. Figure 2: Relative azimuth angles between and sensor 4. INDICES USED FOR ANALYSIS Four different hyperspectral vegetation indices were calculated for the spectral signatures of the ground control points corresponding to the field and laboratory measurements respectively. 4.1. CAI 60 50 40 30 20 10 Position of Red Edge 450 500 550 600 650 700 750 800 Wavelength [ nm ] Figure 3: CAI wavelengths at a vegetation spectrum TCARI OSAVI = 4.4. PRI [( R R ) 0.2 ( R R )] 700nm ( R R )/( R + R + 0.16) 800nm 700nm 800nm 550nm R R 700nm (2) The Photochemical Reflectance Index PRI is proposed as a valuable index to assess leaf physiological properties for a large range of species and is used especially in agricultural applications [11, 12]. It serves as index of the photosynthetic radiation use efficiency and therefore a strong relationship with the chlorophyll content should be existent. ( R531 R PRI = 570 ) ( R531 + R570 ) (3) 5. RESULTS AND DISCUSSION 5.1. Results without considering the directional setting of CHRIS The analysis was conducted using the chlorophyll a content related to two kinds of basis parameter, the leaf biomass and the leaf area. The result of the former is more suitable for agricultural applications because of its relation to a parameter which can be easily measured by
a farmer. The latter is related to a parameter which is common for modelling approaches. At nadir, the top of canopy chlorophyll contents are significantly but weekly correlated with OSAVI and TCARI/OSAVI (Tab.2). The other indices are not able to show significant correlations. The medium forwardlooking angle shows a high potential for estimating chlorophyll a of both and layer. The highest degree of correlation can be observed with the CAI and OSAVI. Significant, but moderate results for chlorophyll are obtained for the +55 angle using the OSAVI. The backward-looking angles are not suitable to derive chlorophyll content neither of the nor of the canopy layer except the PRI which shows a moderately high correlation (r² = 0.42) with chlorophyll using the -36 view angle. Table 2: Determination coefficients (r²) for linear relations between indices and leaf chlorophyll a content [µg/g]; n.s = not significant Nadir Sun Shade N =21 CAI n.s. n.s. OSAVI 0.21 n.s. TCARI/OSAVI 0.39 n.s. PRI n.s. n.s. +36 Sun Shade N =10 CAI 0.70 0.69 OSAVI 0.70 0.71 TCARI/OSAVI 0.56 0.49 PRI 0.43 0.41 +55 Sun Shade N =27 CAI 0.57 n.s. OSAVI 0.62 0.20 TCARI/OSAVI 0.20 0.17 PRI 0.25 n.s. The results for the chlorophyll content per leaf area are different than those presented for leaf biomass. The sample size is relatively small, because the chlorophyll content per leaf area is available only for 2005. This has to be considered for the analysis of the results. Nadir observations are appropriate to assess chlorophyll content of the layer using the PRI or the TCARI/OSAVI (Tab. 3). The TCARI/OSAVI showed also high correlations with chlorophyll content using the moderate forward-looking angle. But the low number of samples has to be considered. All indices are well suited for assessing both the and chlorophyll using the +55 angle. Comparing these results with those for the chlorophyll per biomass, high degrees of correlations can be suggested also for the +36 view angle, but the low number of samples is not appropriate to confirm this assumption. Table 3: Determination coefficients for linear relations between indices and leaf chlorophyll a content [mg/cm²]; n.s = not significant Nadir Sun Shade N =12 CAI 0.44 0.39 OSAVI 0.63 0.52 TCARI/OSAVI 0.71 0.52 PRI 0.67 0.45 +36 Sun Shade N =6 CAI n.s. n.s. OSAVI n.s. n.s. TCARI/OSAVI n.s. 0.88 PRI n.s. n.s. +55 Sun Shade N =18 CAI 0.67 0.60 OSAVI 0.65 0.62 TCARI/OSAVI 0.62 0.52 PRI 0.66 0.54-36 Sun Shade N =12 CAI n.s. n.s. OSAVI n.s. n.s. TCARI/OSAVI n.s. n.s. PRI 0.72 0.39 When using backward-looking view angles, PRI was the only index which is highly correlated with chlorophyll content and weakly correlated with chlorophyll. Otherwise no significant results can be obtained. These results indicate that the chlorophyll a of the canopy layers which is influenced mainly by diffuse radiation does have a significant contribution to the signal which is reflected or absorbed by a wheat canopy. It is possible to distinguish between and chlorophyll using angular CHRIS data. The CAI and OSAVI appear to be more suitable for assessing the chlorophyll contents per biomass. The PRI appears to be the index least affected by directional effects due to changing view angles. The coefficients of determination presented in Tab. 2 and Tab. 3 represent linear relationships. No significant improvements could be obtained by using 2 nd and 3 rd order polynomial or exponential relations.
5.2. Results considering the directional setting of CHRIS It is generally assumed that vegetation indices emphasize differences in the spectral response for different features while reducing the effects of factors such as background substrate, atmosphere, illumination and view angle effects and so enabling multi-temporal comparisons [1,13]. However, studies on broadband [13] as well as on hyperspectral data [14] showed a dependency on illumination geometry. For the CHRIS data used for this study, the sensor azimuths are presented in Fig. 2. For practical reasons it is appropriate to combine the CHRIS data with similar -sensor geometry. The backward-looking CHRIS angles have similar sensor azimuths, but differences exist with the nadir and the forward-looking angles. Thus the data were disposed in clusters with similar illumination geometry which are analysed separately. Tab. 4 shows the results of this analysis for the chlorophyll a content per biomass. Table 4: Determination coefficients for linear relations between indices and leaf chlorophyll a content [µg/g]; n.s. = no significant correlation, the roman numbers indicate the quadrant of the -sensor azimuths Nadir Forward Backward N =11 N = 10 CAI 0.43 n.s. 0.34 0.64 OSAVI 0.45 n.s 0.39 0.65 TCARI/OSAVI 0.48 n.s 0.73 0.73 PRI n.s. 0.41 0.40 0.67 +36 Backward II Backward III N =3 N = 10 CAI n.s. n.s. 0.76 0.69 OSAVI n.s. n.s. 0.76 0.73 TCARI/OSAVI n.s. n.s. 0.66 0.49 PRI n.s. n.s. 0.69 0.53 +55 Backward II Backward III N =23 N = 4 CAI 0.63 0.55 n.s. n.s. OSAVI 0.62 0.53 n.s. n.s. TCARI/OSAVI 0.50 0.51 n.s. n.s. PRI 0.59 0.61 n.s. n.s. For the nadir data, the results could be improved for all indices being the forward-looking nadir more suitable for assessing chlorophyll a for both and layer. Here the TCARI/OSAVI shows the highest correlations. Similar results can be observed for the forward-looking view angles, although the low number of samples inhibits the analysis of some of the data. The best results are achieved by using the moderate forward-looking view angle. The +36 angle show high coefficients of determination for the CAI and the OSAVI, which both are able to differ between lit and d canopy layer. The degree of correlation becomes weaker for the higher view angle (+55). This can be explained with the coarser resolution at higher view angles on the one side and the larger visible fraction of shadow with increasing view angle on the other side. The results are poor for the backward-looking angles. This is accompanied with the observation that the strongest spectral differences are observed in this direction, which is characterized by a predominance of illuminated vegetation components. Backward looking, the anisotropy observed is high in the RED. This is in accordance to the observations of [15,16] that directional effects are particular strong in spectral regions of high absorbance such as the red chlorophyll interval. But a high anisotropy can also be found in the NIR. This can explain the sensitivity of the indices to different view angles, where the PRI showed to be the index least affected. Sun illumination geometry is known to haves a strong effect on the indices [16]. This can be confirmed by the improvement of the results when angular CHRIS data are separated into the different -sensor azimuth settings. Again, the moderate forward-looking view angles showed the best results. The small sample sizes prohibit a statistically firm analysis for the chlorophyll content related to the leaf area and therefore will not be presented. 6. CONCLUSIONS The results demonstrate that it is possible to assess the chlorophyll content of both lit and d layers wheat canopies with angular CHRIS/PROBA data. Especially the CAI and the OSAVI seem to be suitable for this task. The TCARI/OSAVI and the PRI turned out to be better indicators for chlorophyll contents per leaf area than for chlorophyll content per biomass. Especially the moderate forward-looking angle is suitable for chlorophyll estimation having a large contribution of shadowed parts of the canopy but still enough reflected signal to analyse the data. The visible amount of shadow increases with increasing view angle (+55 ) leading to reflectance values near zero in the RED. In addition, the coarser resolution complicates the assignment of pixels to results of ground measurements. Results can be improved when the illumination geometry is considered. Again, the moderate forwardlooking angle is most suitable for chlorophyll assessment, but with a higher degree of correlation
compared to the results without considering illumination differences. Even the so called nadir data that actually do have a slightly backward- or forward-looking view angle show significant correlations with the chlorophyll content by using the forward-looking nadir -part of the data. The backward-looking angles with a predominance of illuminated canopy components are not suitable assessing the chlorophyll content. This is caused by a higher anisotropy observed in backward-looking images compared to the forward-looking data. These directional effects can not be eliminated by using the indices. The PRI seems to be the only index which is not affected as much by changing viewing angles for being the only index with a high degree of correlation (r² = 0.72) for the moderate backward-looking angle. This is due to the fact that the PRI is the only index in the VIS part of the spectrum and therefore is not influenced by anisotropy in the NIR. 7. ACKNOWLEDGEMENTS The authors would like to thank the German Research Foundation (DFG) for funding the project Coupled analysis of vegetation chlorophyll and water content using hyperspectral, bidirectional remote sensing. Thanks are also due to ESA s provision and support of CHRIS data. 8. REFERENCES [1] R.K.J Vincent, Fundamentals of Geological and Environmental Remote Sensing, Prentice Hall, New Jersey (1997) [2] R.J. Porra, W.A. Thomson, P.E. Kriedman, Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophyll a and b extracted with four different solvents: Verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochemical Biophysical Acta, 975, (1989), 384-394. [3] G.A: Blackburn, Quantifying Chlorophylls and Carotenoids at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches, Remote Sensing of Environment, 66 (1998), 273-285 [4] H. Smith, G.C. Whitelam, The avoidance syndrome: multiple responses mediated by multiple phytochromes, Plant Cell Environment, 20 (1997), 840-844. [5] H. Bach, Die Bestimmung hydrologischer und landwirtschaftlicher Oberflächenparameter aus hyperspektralen Fernerkundungsdaten (Assessment of Hydrological Parameters Using Hyperspectral Remote Sensing), Münchner Geographische Abhandlungen, B 21 (1995). [6] A. Berk, G.P. Anderson, P.K. Acharya, J.H. Chetwynd, M.L. Hoke, L.S. Bernstein, E.P. Shettle, M.W. Matthew, S.M. Adler-Golden, MODTRAN 4 Version 2 User Manual, Air Force Research Laboratory, Space Vehicle Directorate, Hanscom AFB, Massachusetts (USA) (2000). [7] Oppelt, N.; Mauser, W., Hyperspectral Monitoring of Physiological Parameters of Wheat During a Vegetation Period Using AVIS Data, Int. Journal of Remote Sensing 25/1 (2004), 145-159. [8] R. Clark, T. King, K. Ager, G. Swayze, Initial Vegetation Species and Senescence/Stress Indicator Mapping in the San Luis Valley, Colorado Using Imaging Spectrometer Data. Proceedings: Summitville Forum 95, Colorado Geological Survey Special Publication 38 (1995), 64 69. [9] G. Rondeaux, M. Steven, F. Baret, Optimization of soil-adjusted vegetation indices, Remote Sensing of Environment, 55 (1996), 95-107 [10] D. Haboudane, J.R. Miller, N. Tremblay, P.J. Zarco- Tejada, L. Dextraze, Integration of hyperspectral vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sensing of Environment, 81 (2002), 416-426. [11] J. Peñuelas, L. Filella, J.A. Gamon, Assessment of photosynthetic radiation use efficiency with spectral reflectance, New Phytologist, 131 (1995), 291-296. [12] J.A. Gamon, L. Filella, C.B. Field, The dynamic 531-nanometer reflectance signal: a survey of twenty angiosperm species, Photosynthetic Responses to the Environment, 1993, 172-177. [13] M.P. Lenney, C.E. Woodstock, J.B. Collins, H. Hamdi, The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM, Remote Sensing of Environment, 56 (1996), 8-20. [14] L.S. Galvao, F.J. Ponzoni, J.C.N. Epiphanio, B.F.T. Rudorff, A.R. Formaggio, Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data. International Journal of Remote Sensing, 25 (1999), 1861-1879. [15] S. Sandmeier, D.W. Deering, Structure analysis and classification of Boreal forests using airborne hyperspectral BRDF data from ASAS, Remote Sensing of Environment, 69 (1999), 281-295. [16] V. Liesenberg, L.S. Galvao, F.J. Ponzoni, Variations in reflectance with seasonality and viewing geometry: implications for classification of Brazilian savanna physiognomies with MISR/Terra data, Remote Sensing of Environment, 107 (2007), 276-286.